Distributed online learning: In practice, a host of application scenarios are dynamic, and data often needs online processing to respond quickly to the real-time needs of users. For example, many people are always keen on online activities, such as watching videos, reading news, shopping, and so on. In order to increase advertising revenue, IT companies have to provide quality ad push services for each user based on their browsing data. As users' online activities are dynamic and uncertain over time, the task of sampling data on all users needs to be performed repeatedly. In consequence, handling nearly petabytes of data every day is their daily routine. Thus, this naturally calls for the study on online learning problem. In recent years, various types of distributed online algorithms have been developed, such as ADMM-based [1]}, [2]}, primal-dual [3]}, [4]}, [5]}, dual-averaging [6]}, [7]}, weight-balancing [8]}, subgradient-push [9]}, mirror descent [10]}, [11]} and so on.
